Intelligent Systems with Applications最新文献

{"title":"Benchmarking deep neural representations for synthetic data evaluation","authors":"Nuno Bento,&nbsp;Joana Rebelo,&nbsp;Marília Barandas","doi":"10.1016/j.iswa.2025.200580","DOIUrl":"10.1016/j.iswa.2025.200580","url":null,"abstract":"<div><div>Robust and accurate evaluation metrics are crucial to test generative models and ensure their practical utility. However, the most common metrics heavily rely on the selected data representation and may not be strongly correlated with the ground truth, which itself can be difficult to obtain. This paper attempts to simplify this process by proposing a benchmark to compare data representations in an automatic manner, i.e. without relying on human evaluators. This is achieved through a simple test based on the assumption that samples with higher quality should lead to improved metric scores. Furthermore, we apply this benchmark on small, low-resolution image datasets to explore various representations, including embeddings finetuned either on the same dataset or on different datasets. An extensive evaluation shows the superiority of pretrained embeddings over randomly initialized representations, as well as evidence that embeddings trained on external, more diverse datasets outperform task-specific ones.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200580"},"PeriodicalIF":4.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145097474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Explainable Artificial Intelligence: A systematic Review of Progress and Challenges","authors":"Azza Mohamed ,&nbsp;Khaled Abdelqader ,&nbsp;Khaled Shaalan","doi":"10.1016/j.iswa.2025.200595","DOIUrl":"10.1016/j.iswa.2025.200595","url":null,"abstract":"<div><div>This work employs a multidisciplinary approach to identify research gaps in the existing literature by presenting a systematic review of systematic reviews on Explainable Artificial Intelligence (XAI). To the best of our knowledge, this is the first thorough meta-review that combines the findings of several excellent reviews to offer a more elevated viewpoint on the goals and difficulties facing the area. The review covers empirical studies published between 2021 and 2023, focusing on high-quality sources. An initial pool of 997 entries was screened across multiple databases, yielding 928 unique articles after duplicate removal. Ultimately, 14 studies met the inclusion criteria and were analyzed in depth. The quality assessment confirmed that all selected reviews adhered to established methodological standards. The key findings show XAI's broad uses, which range from increasing trust and transparency to assisting with financial and management decision-making. The prevalence of healthcare-focused studies emphasizes XAI's importance in enhancing interpretability, fairness, regulatory compliance, and personalized treatment options. Commonly used techniques include visual explanation tools, interpretable machine learning models, and model-agnostic approaches. While the review offers valuable insights, it acknowledges limitations such as its reliance on Q1 journals and the exclusion of broader sources, which may affect comprehensiveness. To advance the field, the study recommends expanding future research to underrepresented domains like autonomous vehicles, defense, and smart cities. It also calls for methodological innovation to enhance accessibility, fairness, privacy, and the development of intuitive explanation strategies. Addressing these gaps can significantly improve the trustworthiness and effectiveness of AI systems across sectors.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200595"},"PeriodicalIF":4.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing the distribution of tasks in Internet of Things using edge processing-based reinforcement learning","authors":"Mohsen Latifi,&nbsp;Nahideh Derakhshanfard,&nbsp;Hossein Heydari","doi":"10.1016/j.iswa.2025.200585","DOIUrl":"10.1016/j.iswa.2025.200585","url":null,"abstract":"<div><div>As the Internet of Things expands, managing intelligent tasks in dynamic and heterogeneous environments has emerged as a primary challenge for processing-based systems at the network’s edge. A critical issue in this domain is the optimal allocation of tasks. A review of prior studies indicates that many existing approaches either focus on a single objective or suffer from instability and overestimation of decision values during the learning phase. This paper aims to bridge this by proposing an approach that utilizes reinforcement learning with a double Q-learning algorithm and a multi-objective reward function. Furthermore, the designed reward function facilitates intelligent decision-making under more realistic conditions by incorporating three essential factors: task execution delay, energy consumption of edge nodes, and computational load balancing across the nodes. The inputs for the proposed method encompass information such as task sizes, deadlines for each task, remaining energy in the nodes, computational power of the nodes, proximity to the edge nodes, and the current workload of each node. The method's output at any given moment is the decision regarding assigning any task to the most suitable node. Simulation results in a dynamic environment demonstrate that the proposed method outperforms traditional reinforcement learning algorithms. Specifically, the average task execution delay has been reduced by up to 23%, the energy consumption of the nodes has decreased by up to 18%, and load balancing among nodes has improved by up to 27%.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200585"},"PeriodicalIF":4.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145097473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced set-based particle swarm optimization for portfolio management in a walk-forward paradigm","authors":"Zander Wessels ,&nbsp;Andries Engelbrecht","doi":"10.1016/j.iswa.2025.200582","DOIUrl":"10.1016/j.iswa.2025.200582","url":null,"abstract":"<div><div>A novel approach to portfolio optimization is introduced using a variant of set-based particle swarm optimization (SBPSO), building upon the foundational work of Erwin and Engelbrecht. Although their contributions advanced the application of SBPSO to financial markets, this research addresses key practical challenges, specifically enhancing the treatment of covariance and expected returns and refining constraint implementations to align with real-world applications. Beyond algorithmic improvements, this article emphasizes the importance of robust evaluation methodologies and highlights the limitations of traditional backtesting frameworks, which often yield overly optimistic results. To overcome these biases, the study introduces a comprehensive simulation platform that mitigates issues such as survivorship and forward-looking bias. This provides a realistic assessment of the modified SBPSO’s financial performance under varying market conditions. The findings shift the focus from computational efficiency to the practical outcomes of profitability that are most relevant to investors.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200582"},"PeriodicalIF":4.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145097476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A data-driven optimization approach for automated reviewer assignment using natural language processing","authors":"Meltem Aksoy ,&nbsp;Seda Yanik ,&nbsp;Mehmet Fatih Amasyali","doi":"10.1016/j.iswa.2025.200587","DOIUrl":"10.1016/j.iswa.2025.200587","url":null,"abstract":"<div><div>In many settings, such as project or publication selection, expert reviewers play a pivotal role, as their assessments serve as the primary basis for determining a project's prospective value. The effectiveness of matching and assigning qualified experts to evaluate project proposals can substantially influence the quality of the selection process and, consequently, impact the funding organization's return on investment. Despite its importance, many funding organizations continue to rely on basic manual methods for assigning reviewers. This simplistic approach can compromise the quality of project selection and lead to suboptimal financial outcomes. Moreover, it may hinder the equitable distribution of review workloads and increase conflicts of interest between reviewers and applicants. Consequently, there is a pressing need for a systematic and automated method to enhance the reviewer assignment process.</div><div>In this study, we propose an optimization-based approach using natural language processing to automate the reviewer assignment process for project proposals. The proposed approach follows a structured three-stage methodology. First, a comprehensive database is constructed by collecting multilingual data on both proposals and reviewers. Second, word embedding techniques are used to represent texts as vectors, enabling the use of cosine similarity to quantify the relevance between each proposal and reviewer. Reviewer expertise and past evaluation performance are also analyzed using predefined knowledge rules. In the final stage, a multi-objective integer linear programming model assigns reviewers by optimizing proposal-reviewer similarity and reviewer competency while preventing conflicts of interest. Additionally, a max-min strategy is employed to ensure fair treatment of less-advantaged proposals, and two supplementary models are introduced to balance reviewer workloads. Experimental results on a real-world dataset from a regional development agency demonstrate that the proposed system significantly outperforms traditional manual assignment methods. We show that automated reviewer assignment prevents subjective judgements, together with reductions in time and cost of the assignment process.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200587"},"PeriodicalIF":4.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Attention-based fuzzy neural networks for self-supervised data annotation","authors":"Md Rakibul Islam,&nbsp;Shahina Begum,&nbsp;Mobyen Uddin Ahmed,&nbsp;Shaibal Barua","doi":"10.1016/j.iswa.2025.200610","DOIUrl":"10.1016/j.iswa.2025.200610","url":null,"abstract":"<div><div>Annotating vibration data from heavy-duty pumps in the mining industry is highly challenging because it demands domain knowledge, a complex inspection setup, and, in many cases, remains infeasible. A self-supervised data annotation (SSDA) framework is therefore proposed and evaluated on historical data of slurry-pump vibration signals. The framework began with the collection of heterogeneous information, followed by information fusion using an autoencoder. This was then followed by a datafication step for preprocessing and achieving a better representation of features through a feature embedding technique. As a result, redundant information was pushed into an eight-dimensional latent space, achieving a reconstruction loss of 0.0023. Furthermore, Initial data annotation was obtained by combining the Isolation Forest and Kneedle algorithms to locate a data-driven knee or threshold, and it was found to be 0.58 for predicting labels. Partial samples were labeled and considered accurate. Lastly, an attention-based fuzzy neural network (AFNN) is trained on those labels where membership functions convert each latent feature into graded truth values. At the same time, an attention layer highlights the most relevant rules. An iterative self-training loop was implemented to refine the training set and obtain labeled data with higher model confidence. Here, we also tested six baseline models and found AFNN quite impressive. After seven iterations 2780 of 2872 samples were labeled and the remaining 92 are considered uncertain, still need some review from an expert, and the AFNN model confidence was (96.8%). Statistical analysis confirmed that the model predictions were significantly associated with true labels (<span><math><mrow><mi>p</mi><mo>&lt;</mo><mn>0</mn><mo>.</mo><mn>05</mn></mrow></math></span>) and not driven by chance.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200610"},"PeriodicalIF":4.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Personalized privacy in OSNs: Evaluating deep learning models for context-aware image editing","authors":"Gelareh Hasel Mehri ,&nbsp;Georgi Kostov ,&nbsp;Bernardo Breve ,&nbsp;Andrei Jalba ,&nbsp;Nicola Zannone","doi":"10.1016/j.iswa.2025.200581","DOIUrl":"10.1016/j.iswa.2025.200581","url":null,"abstract":"<div><div>Online Social Networks (OSNs) have become a cornerstone of digital interaction, enabling users to easily create and share content. While these platforms offer numerous benefits, they also expose users to privacy risks such as cyberstalking and identity theft. To address these concerns, OSNs typically provide access control mechanisms that allow users to regulate content visibility. However, these mechanisms often assume that content is managed by individual users and focus primarily on preserving content integrity, which may discourage users from sharing sensitive information. In this work, we propose a privacy model that empowers users to conceal sensitive content in images according to their preferences, expressed by means of policies. Our approach employs a multi-stage pipeline that includes segmentation for object localization, scene graphs and distance metrics for determining object ownership, and inpainting techniques for editing. We investigate the use of advanced deep learning models to implement the privacy model, aiming to provide personalized privacy controls while maintaining high image fidelity. To evaluate the proposed model, we conducted a user study with 20 participants. The user study highlights that ownership is the most significant factor influencing user perceptions of policy enforcement compliance, with less impact from localization and editing. The results also reveal that participants are generally willing to adopt the fully automated privacy model for selectively editing images in OSNs based on viewer identity, although some prefer alternative use cases, such as editing or censorship tools. Participants also raised concerns about the potential misuse of the model, supporting our choice of excluding an option for object replacement.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200581"},"PeriodicalIF":4.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145267361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing emergency vehicle systems with deep learning: A comprehensive review of computer vision techniques","authors":"Ali Omari Alaoui,&nbsp;Othmane Farhaoui,&nbsp;Mohamed Rida Fethi,&nbsp;Ahmed El Youssefi,&nbsp;Yousef Farhaoui,&nbsp;Ahmad El Allaoui","doi":"10.1016/j.iswa.2025.200574","DOIUrl":"10.1016/j.iswa.2025.200574","url":null,"abstract":"<div><div>Managing emergency vehicles efficiently is critical in urban areas where traffic jams and unpredictable road conditions can delay response times and put lives at risk. Over the years, machine learning methods like k-Nearest Neighbors (k-NN) and Support Vector Machines (SVM), combined with features like HOG and SIFT, paved the way for early image classification and object detection breakthroughs. Tools like Genetic Algorithms (GA) helped refine feature selection, while methods like AdaBoost and Random Forests improved decision-making reliability. The introduction of deep learning has transformed these systems. Convolutional Neural Networks (CNNs) now drive accurate emergency vehicle detection, while Siamese networks support precise identification, such as distinguishing between types of emergency vehicles. Attention mechanisms and Vision Transformers (ViTs) have enhanced the ability to understand context and handle complex scenarios, making them ideal for busy urban environments. Generative Adversarial Networks (GANs) tackle one of the biggest challenges in this field—limited training data—by creating realistic synthetic datasets. This review highlights how these advancements shape emergency response systems, from detecting emergency vehicles in real time to optimizing fleet management. It also explores the challenges of scaling these solutions and achieving faster processing speeds, providing a roadmap for researchers aiming to advance emergency vehicle technologies.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200574"},"PeriodicalIF":4.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145007598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving long-term prediction in industrial processes using neural networks with noise-added training data","authors":"Mohammadhossein Ghadimi Mahanipoor ,&nbsp;Amirhossein Fathi","doi":"10.1016/j.iswa.2025.200579","DOIUrl":"10.1016/j.iswa.2025.200579","url":null,"abstract":"<div><div>Accurate long-term prediction in industrial processes is essential for efficient control and operation. This study investigates the use of artificial neural networks (ANNs) for forecasting temperature in complex thermal systems, with a focus on enhancing model robustness under real-world conditions. A key innovation in this work is the intentional introduction of Gaussian noise into the training data to emulate sensor inaccuracies and environmental uncertainties, thereby improving the network's generalization capability. The target application is the prediction of water temperature in a non-stirred reservoir heated by two electric heaters, where phase change, thermal gradients, and sensor placement introduce significant modeling challenges. The proposed feedforward neural network architecture, comprising 90 neurons across three hidden layers, demonstrated a substantial reduction in long-term prediction error from 11.23 % to 2.02 % when trained with noise-augmented data. This result highlights the effectiveness of noise injection as a regularization strategy for improving performance in forecasting tasks. The study further contrasts this approach with Random Forest model and confirms the superior generalization and stability of the noise-trained ANN. These findings establish a scalable methodology for improving predictive accuracy in industrial systems characterized by limited data, strong nonlinearities, and uncertain measurements.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200579"},"PeriodicalIF":4.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145050158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Indirect visual odometry with a light-field camera","authors":"Mohamad Al Assaad,&nbsp;Stéphane Bazeille,&nbsp;Christophe Cudel","doi":"10.1016/j.iswa.2025.200600","DOIUrl":"10.1016/j.iswa.2025.200600","url":null,"abstract":"<div><div>Visual odometry is the technique of determining a robot’s pose by analyzing images of its surroundings as it moves. Visual odometry can be categorized into monocular when using a single camera, or stereo when using two cameras or more. In this study, we investigate the use of light-field camera for visual odometry. Capitalizing on the distinctive capability of a light-field camera to record both the intensity and the direction of light, we propose an indirect visual odometry method able to estimate the scale of the translation similarly to stereo visual odometry, but using a single camera sensor. Our visual odometry framework combines light-field imaging with conventional odometry techniques to track the camera movements, using the depth insights provided by a light-field depth estimation approach. Additionally, this method differs from state-of-the-art methods by using a simplified calibration process and a new keypoints extraction method, which makes the use of the light-field cameras easier for robotics perception.</div></div>","PeriodicalId":100684,"journal":{"name":"Intelligent Systems with Applications","volume":"28 ","pages":"Article 200600"},"PeriodicalIF":4.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}